Two known types of meiotic recombination are crossovers and gene conversions, which have different effects on the pattern of linkage disequilibrium (LD). Efforts to deduce patterns of historical recombination are central to the design and analysis of disease association studies, which depend on understanding the structure of LD in population data. The focus of the PI's current research is on developing efficient algorithms for reconstructing parsimonious evolutionary histories with recombination. The PI's long-term objective is to characterize quantitatively the effect of various evolutionary forces on shaping the structure of LD in the human genome. Some motivations for the proposed research are as follows: (1) Gene conversion has been hard to study in populations because of the lack of analytical tools and the lack of fine-scale data. However, genomic data produced over the next several years should allow quantification of the fundamental parameters of gene conversion, and the contribution of gene conversion to the overall patterns of sequence variations in a population. (2) Natural selection is an important evolutionary force that shapes genomic variation within species and the divergence between species. It has been shown recently that the patterns of LD generated by strong positive selection can resemble that generated by crossover hotspots. [unreadable] [unreadable] The specific aims of the independent phase of the award are: [unreadable] (1) Develop novel statistical methods for estimating crossover and gene conversion rates. A mathematical framework based on diffusion approximation will be used to obtain novel multi-locus sampling distributions. Gene conversion will be included in that framework. A likelihood method that utilizes the new sampling distributions will be developed to enable joint estimation of crossover and gene conversion rates. [unreadable] (2) Study the effects of natural selection on the pattern of LD. The interaction of selection at multiple loci will be studied analytically and the structure of LD shaped by interacting selection will be characterized. [unreadable] [unreadable] Relevance: Understanding the structure of variation in the human genome is central to the study of the genetic basis of disease risk and variation in drug response. The aim of this research, which is relevant to disease association studies, is to characterize various evolutionary mechanisms that shape the pattern of non-independence of genetic forms at different positions in the genome. [unreadable] [unreadable] [unreadable]